Return fraud is a common problem in the retail industry. Typical return loss is from empty boxes, damaged items, wrong items, return fraud ring, etc. In addition to typical return loss, there is additional labor cost due to inspection and re-stocking. Returns also cause markdowns, out of stocks, and other expenses. Although currently existing systems for collecting return items are configured to detect return frauds, a common issue arises when the systems cannot accept or reject a return item immediately and process a refund. For example, a customer associated with a high amount return item may want to receive a refund immediately upon returning the item, but the systems cannot process an immediate refund because it cannot immediately decide whether the return is fraudulent. This leads to poor customer satisfaction, and a review from the dissatisfied customer may discourage potential sales from other buyers.
To mitigate such problems, conventional electronic systems use return policies (e.g., electronically-enforced rules) to handle return fraud. The policies allow customers to gain a sense of security, which in turn shows a merchant to be one of quality and commitment. However, such policies are usually too strict and inflexible that frequent updates are needed. While these systems attempt to process returns in an efficient manner, many times customers could not receive an immediate and needed refund, and merchants could not reduce fraudulent return loss by taking real-time action accordingly. For example, high risk returns can be rejected immediately to reduce direct money lost or deliver/restocking cost, and low risk returns can receive immediate refund to gain better customer experience.
Therefore, there is a need for improved methods and systems for efficient return fraud detection to reduce return loss.